[1] has set the numActiveColumnsPerInhArea = 240 with 4096 columns and globalInhibition = 1. Wouldn't this produce more than 2% activity?
Pulin Agrawal पुलिन अग्रवाल On Mon, Jan 19, 2015 at 7:54 PM, Subutai Ahmad <[email protected]> wrote: > Hi An, > > Please see [1]. It gets 95.5% accuracy. However, please note this is a > very simplistic system (just SP+KNN). It does not incorporate hierarchy, > temporal pooling, or any sort of learning of invariances. (BTW, anything > less than 99% is not considered very good for MNIST. MNIST is all about > getting those last few corner cases! :-) > > --Subutai > > [1] https://github.com/numenta/nupic.research/tree/master/image_test > > > On Sat, Jan 17, 2015 at 11:00 PM, <[email protected]> wrote: > >> Hello. >> >> Sorry for the last email. Thx to the rich formatting :( ... I have to >> type again. >> >> Recently, I got the result of the test. I followed the source code and >> built the Spatial Pooler + KNN classifier. Then I extracted images from >> MNIST dataset(Train/test : 60000/10000) and parsed them to the model. I >> tried to test with different parameters (using small dataset: Train/Test - >> 6000/1000 ), the best recognition result is about 87.6%. After that, i >> tried the full size MNIST dataset, the result is 89.6%. Currently, this is >> the best result I got. >> >> Here is the statistics. It shows the error counts for each digits. the >> Row presents the input digit. the column presents the recognition result. >> Most of the "7" are recognized as "9". It seems the SDR from SP is still >> not good enough for the classifier. >> >> I found some interesting things. When I let the "inputDimensions" and >> "columnDimensions" be "784" and "1024", the result will be around 68%. If i >> use "(28,28)","(32,32)" and keep others the same, the result will be around >> 82%. That 's a lot of difference. It seems the array shape will effect SP a >> lot. >> >> Did any one get a better result? Does any one have some suggestion about >> the parameters or others? >> >> Thank you. >> An Qi >> Tokyo University of Agriculture and Technology - Nakagawa Laboratory >> 2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588 >> [email protected] >> > >
